Seismic interpretation is a process that aims to investigate the earth subsurface in order to collect relevant information for analysis and to identify hydrocarbon reservoirs in seismic datasets. Seismic interpretation is a time consuming process that is executed by highly skilled interpreters that must deal with the intrinsic uncertainty of the problem. The earth subsurface consists of material layers with distinct mineral densities and porosity characteristics. The interfaces between material layers are called horizons, which are the basic structure for seismic interpretation. Horizons can be analyzed to indicate the existence of faults, stratigraphic structures or structural styles. The identification of such seismic features is an important step in the interpretation of geological and geophysical characteristics of a region underlying subsurface. The task, however, is daunting, due to the nature of seismic data. Seismic datasets are examples of N-dimensional structures with values assigned to each position in the N-dimensional space. The identification of features can occur either in two dimensional datasets, three dimensional datasets or even four dimensional datasets (three spatial coordinates plus time). The automatic search for patterns in these large data structures is a challenge due to the combinatorial nature of the search since, in principle, patterns can be positioned at any point of these large structures (that can have billions of points) and rotations might need to be taken into account. Examples of similar challenges can also be found in other scientific domains such as Astronomy and Biology.
A need exists for methods and apparatus that allow data interpreters to automatically search for features of interest in large N-dimensional datasets, such as a dataset comprised of seismic traces. In the case of seismic data, for example, the features can correspond to the presence of hydrocarbon indicators or other geological features that are important for seismic interpreters. The rapid reconnaissance of points of interest can reduce the uncertainty and speed up the process.